DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

http://dmrocke.ucdavis.edu/Class/BST226.2014.Winter/Nature%20Protocols%20DAVID.pdf

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Correlation networks are increasingly being used in bioinformatics applications For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets These methods have been successfully applied in various biological contexts, eg cancer, mouse genetics, yeast genetics, and analysis of brain imaging data While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software Along with the R package we also present R software tutorials While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings The WGCNA package provides R functions for weighted correlation network analysis, eg co-expression network analysis of gene expression data The R package along with its source code and additional material are freely available at http://wwwgeneticsuclaedu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA
 

/pdf/wgcna-an-r-package-for-weighted-correlation-network-analysis-4vjh9rn783.pdf

WGCNA: an R package for weighted correlation network analysis.

Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

/pdf/bioinformatics-enrichment-tools-paths-toward-the-2yo1xl3r2j.pdf

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

Tissue-based map of the human proteome

We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w1118; iso-2; iso-3 strain and the reference y1; cn1 bw1 sp1 strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in...

A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3

The distributed nature of biological knowledge poses a major challenge to the interpretation of genome-scale datasets, including those derived from microarray and proteomic studies. This report describes DAVID, a web-accessible program that integrates functional genomic annotations with intuitive graphical summaries. Lists of gene or protein identifiers are rapidly annotated and summarized according to shared categorical data for Gene Ontology, protein domain, and biochemical pathway membership. DAVID assists in the interpretation of genome-scale datasets by facilitating the transition from data collection to biological meaning.

/pdf/david-database-for-annotation-visualization-and-integrated-51hh9rj9ic.pdf

DAVID: Database for Annotation, Visualization, and Integrated Discovery

Seed dormancy and germination determine wheat resistance to pre-harvest sprouting and thereby affect grain yield and quality. Arabidopsis VQ genes have been shown to influence seed germination; however, the functions of wheat VQ genes have not been characterized.We identified 65 TaVQ genes in common wheat and named them TaVQ1-65. We identified 48 paralogous pairs, 37 of which had Ka/Ks values greater than 1, suggesting that most TaVQ genes have experienced positive selection. Chromosome locations, gene structures, promoter element analysis, and gene ontology annotations of the TaVQs showed that their structures determined their functions and that structural changes reflected functional diversity. Transcriptome-based expression analysis of 62 TaVQ genes and microarray analysis of 11 TaVQ genes indicated that they played important roles in diverse biological processes. We compared TaVQ gene expression and seed germination index values among wheat varieties with contrasting seed dormancy and germination phenotypes and identified 21 TaVQ genes that may be involved in seed dormancy and germination.Sixty-five TaVQ proteins were identified for the first time in common wheat, and bioinformatics analyses were used to investigate their phylogenetic relationships and evolutionary divergence. qRT-PCR data showed that 21 TaVQ candidate genes were potentially involved in seed dormancy and germination. These findings provide useful information for further cloning and functional analysis of TaVQ genes and introduce useful candidate genes for the improvement of PHS resistance in wheat. 

/pdf/characterization-of-the-wheat-vq-protein-family-and-2dnj6we6.pdf

Characterization of the wheat VQ protein family and expression of candidate genes associated with seed dormancy and germination

/pdf/characterization-of-the-wheat-vq-protein-family-and-1hfk15zb.pdf

Protein fluorescence reporting systems are of crucial importance to in-depth life science research, providing systematic labeling tools for visualization of microscopic biological activities in vivo and revolutionizing basic research. Cotton somatic cell regeneration efficiency is low, causing difficulty in cotton transformation. It is conducive to screening transgenic somatic embryo using the fluorescence reporting system. However, available fluorescence labeling systems in cotton are currently limited. To optimize the fluorescence reporting system of cotton with an expanded range of available fluorescent proteins, we selected 11 fluorescent proteins covering red, green, yellow, and cyan fluorescence colors and expressed them in cotton. Besides mRuby2 and G3GFP, the other nine fluorescent proteins (mCherry, tdTomato, sfGFP, Clover, EYFP, YPet, mVenus, mCerulean, and ECFP) were stably and intensely expressed in transgenic callus and embryo, and inherited in different cotton organs derive from the screened embryo. In addition, transgenic cotton expressing tdTomato appears pink under white light, not only for callus and embryo tissues but also various organs of mature plants, providing a visual marker in the cotton genetic transformation process, accelerating the evaluation of transgenic events. Further, we constructed transgenic cotton expressing mCherry-labeled organelle markers in vivo that cover seven specific subcellular compartments: plasma membrane, endoplasmic reticulum, tonoplast, mitochondrion, plastid, Golgi apparatus, and peroxisome. We also provide a simple and highly efficient strategy to quickly determine the subcellular localization of uncharacterized proteins in cotton cells using organelle markers. Lastly, we built the first cotton stomatal fluorescence reporting system using stomata-specific expression promoters (ProKST1, ProGbSLSP, and ProGC1) to drive Clover expression. The optimized fluorescence labeling system for transgenic somatic embryo screening and functional gene labeling in this study offers the potential to accelerating somatic cell regeneration efficiency and the in vivo monitoring of diverse cellular processes in cotton.

Optimizing the Protein Fluorescence Reporting System for Somatic Embryogenesis Regeneration Screening and Visual Labeling of Functional Genes in Cotton

Wei Gao

Papers

DAVID: Database for Annotation, Visualization, and Integrated Discovery

Characterization of the wheat VQ protein family and expression of candidate genes associated with seed dormancy and germination

Characterization of the wheat VQ protein family and expression of candidate genes associated with seed dormancy and germination

Optimizing the Protein Fluorescence Reporting System for Somatic Embryogenesis Regeneration Screening and Visual Labeling of Functional Genes in Cotton